Multi-dimensional spectroscopic NMR and MRI using marginal distributions

ABSTRACT

Multi-dimensional spectra associated with a specimen are reconstructed using lower dimensional spectra as constraints. For example, a two-dimensional spectrum associated with diffusivity and spin-lattice relaxation time is obtained using one-dimensional spectra associated with diffusivity and spin-lattice relaxation time, respectively, as constraints. Data for a full two dimensional spectrum are not acquired, leading to significantly reduced data acquisition times.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation application of U.S. Pat. App.No. 16/324,413, filed Feb. 8, 2019, which is the U.S. national phase ofInternational Patent Application No. PCT/US2017/046615, filed Aug. 11,2017, which claims the benefit of U.S. Provisional Application No.62/373,497, filed Aug. 11, 2016, which is herein incorporated byreference in its entirety.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with Government support under project numberHD000266-17 by the National Institutes of Health, National Institute ofChild Health and Development. The Government has certain rights in theinvention.

FIELD

This disclosure relates to nuclear magnetic resonance and magneticresonance imaging.

SUMMARY

Measuring multidimensional (e.g., 2D) relaxation spectra in nuclearmagnetic resonance (NMR) and magnetic resonance imaging (MRI) clinicalapplications has numerous applications. NMR and MRI methods and systemsare collectively referred to herein as “MR” methods and system. A mainbottleneck has been the need for significant computational resources toprocess large data sets and long acquisition times required to obtainthese data sets. Typical methods are based on inversion of Fredholmintegrals of the first kind, an ill-conditioned problem requiring largeamounts of data to stabilize a solution.

In this disclosure, novel MR methods and approaches are disclosed thatcan accelerate the acquisition and improve the reconstruction of such 2Dspectra using a priori information from 1D projections of spectra, ormarginal distributions. In the disclosed approaches, these 1D marginaldistributions provide powerful constraints when 2D spectra arereconstructed, and typically require an order of magnitude less datathan a conventional 2D approach. The disclosed approaches can bereferred to for convenience as marginal distributions constrainedoptimization (MADCO) methods. In an example, a representative MADCOmethod is described with reference to a polyvinylpyrrolidone-waterphantom that has three distinct peaks in the 2D D-T₁ space. Thestability, sensitivity to experimental parameters, and accuracy of thisnew approach are compared with conventional methods by seriallysubsampling the full data set. While the conventional, unconstrainedmethod performed poorly, the new method is highly accurate and robust,only requiring a fraction of the data. Some aspects of the disclosedtechnology can also be more generally applied, and may be used with avariety of 2D MRI experiments (D-T₂, T₁-T₂, D-D, etc.), making thesepotentially feasible for biological, preclinical and clinicalapplications for the first time.

In some examples, methods comprise acquiring a selected set of MRspecimen data by varying at least a first MR acquisition parameter and asecond MR acquisition parameter over first and second ranges,respectively, so that the acquired MR specimen data is associated with afirst specimen characteristic and a second characteristic. Based on theacquired MR specimen data, a first marginal distribution and a secondmarginal distribution associated with the first and second specimencharacteristics are determined, wherein the first and second marginaldistributions are dependent on a selected combination of the first andsecond MR acquisition parameters. A two-dimensional spectrum associatedwith the specimen is reconstructed using the first and second marginaldistributions as constraints. In some examples, the first marginaldistribution and the second marginal distribution are one-dimensionalmarginal distributions associated with the first MR acquisitionparameter and the second MR acquisition parameter, respectively.

In other examples, the first specimen characteristic is ω₁ and thesecond specimen characteristic is ω₂. The variables ω_(i) can bespin-lattice relaxation time (T₁), spin-spin relaxation time (T₂),transverse relaxation time (T₂*), diffusivity (D), magnetizationtransfer (MT), or combinations thereof. In still further examples, thefirst MR acquisition parameter is β₁ and the second MR acquisitionparameter is β₂. Depending on the measured sample characteristic ω_(i),the acquisition parameter β_(i) can be the inversion time τ₁, the echotime τ₂, the diffusion-weighting b-value, and the mixing time τ_(m).

In other examples, the first MR acquisition parameter and the second MRacquisition parameter are independent. In other embodiments, the firstand second marginal distributions are determined using l₁ or l₂regularization. In further representative examples, the set of MR datato be acquired is selected so as to determine the first and secondmarginal distributions. In yet other embodiments, the selected set of MRspecimen data is a first set of MR data, a second selected set of MRdata is acquired, and the first and second marginal distributions aredetermined based on the first selected set of MR data and the secondselected set of MR data. In some examples, at least one computerreadable media has stored thereon computer-executable instructions forperforming such methods.

In other examples, an MR system comprises an MR data acquisition systemand an MR data processor coupled to the MR data acquisition system. TheMR data processor is operable to reconstruct two-dimensional MR spectraassociated with specimens using first and second marginal distributionsas constraints. In some examples, the MR data acquisition system isoperable to acquire MR data based on applied MR signals associated withfirst and second MR acquisition parameters. In representativeembodiments, the first and second MR acquisition parameters are operableto produce MR signal variations associated with a first specimencharacteristic and a second specimen characteristic. In otherembodiments, the MR data acquisition system includes at least a magnetsituated to produce a static magnetic field and a radio-frequency pulsegenerator operable to induce specimen spin rotations. In typicalexamples, the MR data processor is operable to determine the first andsecond marginal distributions using l₁ or l₂ regularization.

More generally, the disclosed examples can be extended from NMR to MRIexamples and applications in which the NMR experiments can be combinedwith spatial localization or imaging MR sequences to permit thisinformation to be obtained voxel-by-voxel.

The foregoing and other features and advantages of the disclosedtechnology will become more apparent from the following detaileddescription, which proceeds with reference to the accompanying figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate a conventional scheme (1A) and an exemplarymarginal distributions constrained optimization (MADCO) experimentaldesign scheme (1B) used to obtain a 2D correlation function, F(ω₁, ω₂).

FIG. 2 shows a ground truth T₁-D distribution obtained from a separateanalysis of each (T₁, D) sample and averaging according to the relativespin density. The three peaks are identified and numbered (1, 2, 3) forreference, and their (gmT₁, gmD) values were 1. (293 ms, 2.26 μ²/ms); 2.(782 ms, 1.99 μ²/ms); and 3. (1596 ms, 1.24 μ²/ms).

FIGS. 3A-3I illustrate reconstruction with an unconstrained conventionalmethod. Two data subsamples and the full data set resulted in the 2DD-T₁ distributions in 3A, 3B, and 3C, respectively. FIGS. 3D-3I show the1D projections (full blue circles) overlaid with the ground truth 1Ddistributions (red empty circles).

FIGS. 4A-4I illustrate reconstruction with a representative MADCOmethod. Two data subsamples and the full data set resulted in the 2DD-T₁ distributions in 4A, 4B, and 4C, respectively. FIGS. 4D-4I show the1D projections (full blue circles) overlaid with the ground truth 1Ddistributions (red empty circles).

FIGS. 5A-5C illustrate the accuracy and stability typical of thedisclosed methods demonstrated by the Jensen difference betweenestimated and ground truth distributions, with the conventional (greendiamonds) and MADCO (blue squares) methods, as a function of the numberof acquisitions. Measured distances of the 2D D-T₁ distribution, andprojections of the T₁ and D distributions, are shown in FIG. 5A, FIG.5B, and FIG. 5C, respectively.

FIG. 6 shows the T₁-D distribution with l₂ regularization using aconventional method on a full data set.

FIGS. 7A and 7B are graphs showing comparison between ground truth(empty red circles) and estimations from 1D experiments (full bluecircles). FIG. 7A shows a marginal T₁ distribution and FIG. 7B shows amarginal D distribution, obtained from 1D acquisitions.

FIG. 8 is schematic illustration of an exemplary magnetic resonanceapparatus.

FIG. 9 is a flow chart illustrating an exemplary method of collectingmagnetic resonance data.

FIG. 10 is a schematic diagram illustrating a representativecomputational environment for performing data acquisition, storage,analysis, and other operations discussed herein.

DETAILED DESCRIPTION Introduction

Multidimensional nuclear magnetic resonance (NMR) experiments allowdetermination of correlations between relaxation properties such as T₁and T₂, and physical parameters, such as diffusivity D. Thesecorrelations can be used to identify and characterizemicrostructure-related water dynamics in many applications. Thefollowing general expression describes the received signal M (alsocommonly referred to as signal attenuation) from 2D NMR experimentsassociated with sample parameters col, cot with separable kernels:M(β₁,β₂)=∫∫F(ω₁,ω₂)K ₁(β₁,ω₁)K ₂(β₂,ω₂)dω ₁ ,dω ₂  (1)wherein F(ω₁, ω₂) corresponds to a sample distribution, K₁(β₁, ω₁) andK₂(β₂, ω₂) are so-called kernels associated with signal dependence onthe associated parameters, and β₁ and β₂ are experimental parametersthat are determined by the data acquisition scheme. Eq. 1 is an exampleof a broad class of Fredholm integrals of the first kind. When thekernels K₁, K₂ have an exponential form, application of a 2D inverseLaplace transform (ILT), which is a classic ill-conditioned problem, isrequired to obtain the sample distribution F(ω₁, ω₂). The most common2D-ILT algorithm used in 2D relaxometry experiments that involve aCarr-Purcell-Meiboom-Gill (CPMG) acquisition requires high densitysampling of the received signal. This algorithm can greatly improve theefficiency of the inversion by compressing the 2D signal without losinguseful information, revealing a redundancy in some basisrepresentations.

Although multi-dimensional diffusion/relaxation experiments have been ofgreat interest in recent years, preclinical and clinical applicationsare currently infeasible. In high-field 3T and 7T MRI scanners, thetotal number of 180° pulses that can be applied per unit time is limitedby safety concerns, primarily due to high specific absorption rate(SAR). Fast spin-echo, multi-echo, or CPMG pulse trains are thereforenot clinically applicable, and the large amounts of data required cannotbe collected in in vivo experiments due to long scan times. Eachacquisition, whether D-T₁, T₁-T₂ measurements absent a CPMG pulse train,would result in only a single experimental data point. When apotentially lengthy imaging block is added, shortening the scan timebecomes a primary challenge.

Accordingly, in some examples the number of acquisitions needed for anaccurate 2D diffusion/relaxation spectrum reconstruction issubstantially reduced. One exemplary approach to is to use marginal 1Ddistributions of the desired 2D function as equality constraints tostabilize and reduce the number of acquisitions needed to invert adiscrete Fredholm equation. Applying marginal distributions constrainedoptimization (MADCO) to multidimensional NMR experiments, 1D projectionsof a 2D correlation function of two relaxation/diffusion parameters canbe used to greatly constrain the solution space of F(ω₁, ω₂).

One exemplary measurement and reconstructive approach is illustrated inFIGS. 1A-1B. Instead of sampling the entire experimental parameter space(β₁, β₂) (FIG. 1A) and estimating the 2D distribution F(ω₁, ω₂), usingthe exemplary MADCO approach illustrated in FIG. 1B, the 2D distributioncan be estimated accurately with a relatively dense sampling along β₁and β₂ axes (i.e., 1D data), complemented with a small number ofacquisitions in the 2D space. The 2D reconstruction then typicallycomprises two steps: (1) estimating F(ω₁) and F(ω₂) from the 1D data,and then (2) using these 1D spectra to constrain the estimation of F(ω₁,ω₂) from the remaining 2D data.

Typically, to obtain multi-dimensional spectra of particular specimencharacteristics (such as D and T₁) associated NMR signal acquisitionparameters such as gradient strength G (or equivalently, b-value) andinversion time τ are varied to provide one dimensional spectra. In someexamples, one dimensional spectra are obtained with one or both of theNMR signal acquisition parameters set to a fixed value thatsubstantially reduces or eliminates NMR acquired signal dependence onthe corresponding specimen characteristic. For example, as discussedbelow, gradient strength can be set to 0 or otherwise made small, andinversion time can be very long with respect to specimen T₁ values.Although not as straightforward, NMR signals that are dependent oncombinations of specimen characteristics can be obtaining usingdifferent combinations of the associated acquisition parameters. Forexample, different combinations of b-values and inversion times τ can beapplied such as, for example, linearly independent combinations ofacquisition parameters such as Ab+Bτ and Ab−Bτ, wherein A, B arearbitrary numbers. The corresponding one-dimensional spectra are thenassociated with these combined characteristics and can be used toconstrain multi-dimensional spectra. In other examples, arbitrary setsof acquired data are used to produce one-dimensional spectra. Althoughnot discussed in detail, multi-dimensional spectra of a lower order canbe used to constrain the determination of higher order multi-dimensionalspectra. For example, a one dimensional spectrum and a two dimensionalspectrum can be used to constrain a three dimensional spectrum. Thus,two lower order spectrum associated with n specimen characteristics andm specimen characteristics, respectively, can be used to find an n+mspectrum.

Although the disclosed methods can be equivalently applicable to othertypes of multidimensional experiments, for convenient illustration, D-T₁measurements of a polyvinylpyrrolidone (PVP) water solution phantom aredescribed herein. A clinically applicable inversion recoverydiffusion-weighted imaging pulse sequence was used to observe D-T₁correlations. The disclosed and conventional approaches wereinvestigated to determine the stability, sensitivity to experimentalparameters, and accuracy of both methods were explored by incrementallydecreasing the number of acquisitions by subsampling a full data set.

Sample Preparation and Data Acquisition

Doped water and polyvinylpyrrolidone PVP (Sigma-Aldrich, K value 29-32)were used to create a D-T₁ phantom with three distinct peaks. Aqueoussolutions of PVP were shown to make good diffusion MR phantoms sincetheir measured diffusivity is independent of the diffusion time,indicating Gaussian diffusion of a single population of spins. Inaddition, increasing PVP weight per volume (w/v) concentration isnegatively correlated with both the diffusivity and T₁. Two purifiedwater samples with 0.18 mM and 0.5 mM gadopentetate dimeglumine wereprepared, along with a 20% w/v PVP water solution sample. Thecorresponding weighted geometric means (gm) of the relaxation times anddiffusivities (gmT₁, gmD), as measured separately for each sample areshown in FIG. 2 . Each sample was placed in a 4 mm NMR tube; these werethen inserted together into a 15 mm NMR tube.

Imaging data were collected on a 7 T Bruker wide-bore vertical magnetwith an AVANCE III MRI spectrometer equipped with a Micro 2.5microimaging probe. MRI data were acquired with an inversion recoveryspin-echo diffusion-weighted echo planar imaging (IR-DWI-EPI) sequence,with an adiabatic 180° inversion pulse applied before the standardspin-echo diffusion weighted sequence. The full 2D experimental set had40 diffusion gradient linear steps (G) ranging from 0 to 900 mT/m, 37inversion times (τ) with logarithmic temporal spacing ranging from 100to 3000 ms, and an additional magnetization equilibrium scan with aninversion time of 10 s. The 1D experiments were a subset of the full 2Ddata set. The 1D IR data set included all of the 37 inversion times withG=0, and the 1D diffusion data set included all of the odd diffusiongradient linear steps (total of 20) with τ=10 s. Other acquisitionparameters were diffusion gradient duration and separation of 8=3 ms andΔ=15 ms, respectively, leading to a b-value range of 0 to 6200 s/mm²(b=γ² δ²G²(Δ−δ/3)), where γ is the gyromagnetic ratio, TE=50 ms, andTR=inversion time+10 s. A single 5 mm axial slice with a matrix size andresolution of 64×64 and 0.2×0.2 mm, respectively, was acquired with twoaverages and four segments. The experimental signal-to-noise ratio (SNR)in the full 2D experiment was ˜700.

Methods

For D-T₁ measurements, for a given recovery time the fully recovereddata are subtracted from the data set so that the potential artifactscaused by imperfect inversion pulses are cancelled. Eq. 1 (above) can bediscretized by using the kernels K₁ (τ, T₁) and K₂(b, D), with N_(T1)and N_(D) log-spaced values of T₁ and D, respectively, and N_(τ) andN_(b) values of τ and b, respectively:

$\begin{matrix}{{M( {\tau,b} )} = {\sum_{n = 1}^{N_{T_{1}}}{\sum_{m = 1}^{N_{D}}{{F( {T_{1,n},{Dm}} )}{\exp( {- \frac{\tau}{T_{1,n}}} )}{\exp( {- {bD}_{m}} )}}}}} & (2)\end{matrix}$wherein F(T_(1,n), D_(m)) is a discretized sample density and the 2Dkernel, K(τ, b, T₁, D)=K₁(τ, T₁)K₂(b, D). Eq. 2 can be written in matrixform as:M=KF,  (3)where M and F are (N_(τ)N_(b))×1 and (N_(T1)N_(D))×1 vectors, and K isan (N_(τ)N_(b))×(N_(T1)N_(D)) matrix. As discussed, Eq. 3 represents anill-conditioned problem, i.e., a small change in M may result in largevariations in F. One approach to solving such ill-conditioned problemsis regularization. If a solution is expected to be smooth, l₂regularization is appropriate. However, in this example the phantomcomprises discrete components in D-T₁ space, making l₁ regularization amore suitable choice since it has many of the beneficial properties ofl₂ regularization, but yields sparse models (see “Marginal DistributionsConstrained Optimization (MADCO) for Accelerated 2D MRI Relaxometry”below). The regularized problem considered in this example was:F ^((α))=argmin_(F≥0)(∥KF−M∥ ₂ ² +α∥F∥ ₁ ²)  (4)wherein ∥ . . . ∥₂ and ∥ . . . ∥₁ are l₂ and l₁ norms, respectively, anda regularization parameter a was chosen based on an S-curve method,which uses a fit error, χ(α)=∥KF^((α))−M∥₂. The regularization parameterwas determined such that d(log χ)/d(log α)=TOL, with TOL=0.1.

The disclosed approach provides simple and elegant ways to stabilize thesolution of Eq. 4, while significantly reducing the number of requireddata acquisitions (i.e., to reduce N_(τ) and N_(b)) and improvingaccuracy. Since F(T₁,D) is the joint distribution of T₁ and D, it isrelated to the 1D marginal distributions F(T₁₎ and F(D) by

$\begin{matrix}{{\sum\limits_{n = 1}^{N_{D}}{F( {T_{1},D_{n}} )}} = {{{F( T_{1} )}{and}{\sum\limits_{n = 1}^{N_{T_{1}}}{F( {T_{1},n,D} )}}} = {{F(D)}.}}} & (5)\end{matrix}$

The 1D marginal distributions, F(T₁) and F(D), can be separatelyestimated from 1D inversion recovery or diffusion experiments,respectively, and then used to find F(T₁,D) by applying the two parts ofEq. 5 as equality constraints when Eq. 4 is solved. The 1D problems, inwhich Eq. 4 is solved by replacing the (N_(τ)N_(b))×(N_(T1)N_(D)) kernelK with N′_(τ)×N_(T1) kernel K₁ or N′_(b)×N_(D) kernel K₂(N′_(τ) andN′_(b) are the number of 1D acquisitions), reduce the number of freeparameters by a factor of N_(D) and N_(T1), respectively.

Using MADCO is shown here to dramatically reduce the number of 2Dexperiments required to estimate the 2D distribution, such thatN_(τ)<<N′_(τ) and N_(b)<<N′_(b). These equality constraints should berelaxed when experimental noise is expected to result in inaccurateF(T₁) and F(D) estimations. In this case the constraints are

$\begin{matrix}{{\frac{1}{N_{D}}{{{\sum\limits_{n = 1}^{N_{D}}{F( {T_{1},D_{n}} )}} - {F( T_{1} )}}}_{2}} < \sigma} & (6)\end{matrix}$ and $\begin{matrix}{{{\frac{1}{N_{T_{1}}}{{{\sum\limits_{n = 1}^{N_{T_{1}}}{F( {T_{1,n},D} )}} - {F(D)}}}_{2}} < \sigma},} & (7)\end{matrix}$where σ is the standard deviation (SD) of the noise (as determined aftercomplete signal decay) normalized by the unattenuated signal. Similarlyto the standard non negativity constraints, the inequality constraintsin Eqs. 6 and 7 also represent physical conditions that must befulfilled (“conservation of mass” of the 2D probability distributionprojected onto one of its axes), and can be applied in a similar manner.In the disclosed example, N_(T1)=N_(D)=50, N′_(τ)=37, N′_(b)=20, and thenumber of 2D experiments (N_(τ)N_(b))_(were) varied in the range of 7 to1480, while stability and accuracy were quantified.

Imaging the phantom enabled separate analysis of each of the (T₁, D)samples by the selection of three regions of interest (ROI). Solving Eq.6 using the full 2D dataset and the 1D subsets resulted in estimationsof F_(GT) (T₁, D), F_(GT)(T₁), and F_(GT)(D), (GT stands for groundtruth) from these ROIs, separately. Estimating the spectra of a singlepeak data (i.e., monoexponential) is a well-posed problem, and thereforethe 1D and 2D distributions obtained from these analyses were averagedaccording to their relative spin density and taken as the ground truth(FIG. 2 for a 2D distribution, and FIG. 6 for a 1D distribution).Performance was assessed by computing the Jensen difference, which is ameasuring of distance between two probability distributions. The Jensendifference d_(JD) is a symmetric version of the Kullback-Leiblerdivergence that is bounded by 0 and 1 and is defined for twodistributions, Q and P, as:

$\begin{matrix}{d_{JD} = {\sum\limits_{i}{\lbrack {\frac{{P_{i}{\ln( P_{i} )}} + {Q_{i}{\ln( Q_{i} )}}}{2} - {( \frac{P_{i} + Q_{i}}{2} ){\ln( \frac{P_{i} + Q_{i}}{2} )}}} \rbrack.}}} & (8)\end{matrix}$

Results

The performance of MADCO was determined and compared with theconventional method by estimating the D-T₁ distribution by using 500random subsamples from the full data set at 19 logarithmicallydistributed acquisitions. Reconstruction using the conventionalexperimental design (FIG. 1A) at three representative datasubsamples—64, 157, and 1480 acquisitions—resulted in the 2D D-T₁distributions shown in FIGS. 3A-3C, respectively. In FIGS. 3D-3I, foreach 2D distribution the 1D projections are overlaid with the groundtruth. Visually compared with the ground truth in FIG. 2 , the estimatedresults in FIGS. 3A-3I are highly inaccurate, both for the 2D and 1Dprojections; furthermore, they remain poor even when the full data setis used (1480 acquisitions).

Spectra reconstructed using the MADCO experimental design (FIG. 1B) andprocessing framework at the same three data subsamples (in this case 7,100, and 1423 2D acquisitions along with 20 and 37 1D diffusion (O) andIR (T₁₎ acquisitions, respectively, totaling 64, 157, and 1480acquisitions) are shown in FIGS. 4A-4I. Stability, as well as accuracy,are evident from the 2D distributions, in which little to no differencewas noted as the number of acquisitions was increased by a factor of 25(FIGS. 4A-4C). The marginal T₁ and D distributions were more accurate at157 acquisitions (FIGS. 4E and 4H) than with the full data set. Thisobservation can be attributed to the nonuniform (sub)sampling, which isknown to yield an increase in SNR. In terms of establishing the peaks(mean and amplitude) accuracy, MADCO outperformed the conventionalmethod in almost every instance, while consistently keeping thenormalized root mean square error well-below 10%. A more complete listof parameters and details regarding the error computation are providedin Tables 1 and 2 below.

As opposed to single parameters (e.g., gmT₁, gmD) accuracy, it is moreinformative to study the accuracy of the entire reconstructed 2Ddistribution as a function of the number of acquisitions. Jensendifferences between the estimated and the ground truth distributionswere calculated after reconstruction of the 2D distributions with andwithout MADCO. For each number of acquisitions, averages and StandardDeviations (SDs) of the 2D Jensen differences from the 500 randomsubsamples were computed and are presented in FIGS. 5A-5C. Both theJensen difference between the 1D projections of the T₁ and Ddistributions and the ground truth are shown in FIGS. 5B and 5C,respectively. The high accuracy of MADCO reconstruction is demonstratedby its markedly low Jensen difference from the ground truth, comparedwith results from the unconstrained approach. The stability of MADCO isevident from the relatively small SD and its low variability.Conversely, the conventional method resulted in a large SD that pointsto a high degree of sensitivity to experimental parameters that wasmitigated by using MADCO. It is worth noting that in some cases theJensen distance of the projected distributions did not alwaysmonotonically decrease as a function of increased number ofacquisitions. In both cases, however, the 2D Jensen distance exhibited amonotonic decrease.

DISCUSSION

Despite the ill-conditioned nature of the numerical inversion of theFredholm integrals of the first kind, this problem is at the heart ofmany applications. The resultant data bottleneck in many areas thatrequire a large number of experiments and samples has made manyapplications impractical or infeasible. For example, although 2D NMRexperiments are very powerful, because of the long acquisition timerequired to obtain sufficient data to invert the Fredholm equation,these experiments have not been migrated to in vivo preclinical orclinical MRI applications. The technology described herein canstabilize, accelerate, and improve the reconstruction of a 2D spectrumby using the more easily accessible knowledge about its 1D projections.MADCO uses a general mathematical approach for improving the propertiesof ILT and has been demonstrated here on a D-T₁ phantom sample comprisedof three distinct peaks in a 2D space. As shown, provided a reasonablyaccurate estimation of the 1D marginal distributions, constraining Eq. 4with the inequalities in Eqs. 6 and 7 resulted in a high level ofaccuracy while using a fraction of the full data set. The efficiency ofMADCO can be expressed by considering the number of dimensions, N, andthe number of acquisitions required to reconstruct the 1D spectrum in agiven dimension, M A conventional approach requires O(MN) measurements,while using MADCO only O(MN) acquisitions are needed.

The independent 1D measurements resulted in highly accurate marginaldistributions, even for the demanding three-peak 1D T₁ distribution (seeFIG. 6 ). The 1D projections from the conventionally obtained 2Ddistributions (FIGS. 3D-3I) were inaccurate and not robust, especiallyin resolving the more challenging T₁ distribution. The surprisingresults disclosed herein are in contrast to published reports whichstate that recovering a 1D relaxation distribution by first performing a2D relaxometry experiment followed by projection is more accurate andreliable than 1D relaxometry. However, prior approaches have failed torecognize that l₂ norm regularization is unsuitable, particularly foranalyzing data acquired from phantoms comprised discrete components in a2D parameter space.

Purposely oversampled, the full acquisition scan time was ˜37 h. Asshown, the D-T₁ distribution estimated with the conventional method wasfar from accurate, even when the full data set was used. Conversely,applying the disclosed methodology led to good agreement with the groundtruth distribution, even when using only 4% of the full data set (64acquisitions). Furthermore, additional acquisitions may still benecessary to improve the accuracy of the conventional approach leadingto further convergence in the Jensen distance shown in FIG. 5A. Sinceconvergence was not reached, it is difficult to establish the trueacceleration factor of MADCO although it is logically greater than1480/64≈25. In the disclosed example 64 acquisitions ˜90 min scan timeswere sufficient for a complete D-T₁ distribution mapping using the MADCOmethod. Although a phantom was selected for convenient illustrationstudy, an IR-DWI-EPI pulse sequence was used which can be directlyapplied to in vivo preclinical and clinical studies. The conservativelychosen repetition time used in this example (10 s) could be reduced.Further acceleration can be achieved by using a Look-Locker acquisitionfor T₁-weighting, or modified versions thereof. Recent advances insampling strategies, such as compressed sensing for relaxometryparameter space, may also be integrated with the MADCO method toincrease 2D acquisition efficiency.

Among other biological applications, characterization of nerve tissuewith 2D diffusometry/relaxometry MRI may be able to reveal otherwiseinaccessible information. Preliminary findings from D-T₁ MRI experimentson nerve tissue analyzed with MADCO indicate sensitivity tomicrostructural features and complex water exchange dynamics, suchmolecular exchange dynamics. The disclosed technology can also provide acomprehensive investigation in a reasonable time frame by using theMADCO method in conjunction with a variety of other 2D experiments, suchas D-T₂ and T₁-T₂ correlation and T₂-T₂ and D-D exchange studies.Furthermore, the disclosed technology may be extended beyond 2D, sinceapplication of the marginal distributions constrained optimizationprinciple in higher dimensions enables the main limitation ofexperimental time to be lifted.

Use of Marginal Distributions Constrained Optimization (MADCO) forAccelerated 2D MRI Relaxometry Using l₂ Regularization

When a spectrum is expected to be smooth, l₂ regularization isappropriate. However, in the disclosed example, the phantom comprisesdiscrete components in D-T₁ space, making l₁ regularization a moresuitable choice since it has many of the beneficial properties of l₂regularization, but yields sparse models. To illustrate, l₂regularization was employed on the full data set (1480 acquisitions)using the conventional method. The resulting spectrum is shown in FIG. 6.

Ground Truth and Estimations from 1D Experiments

Imaging the phantom allowed separate analysis of each of the (T₁, D)samples by selecting 3 regions of interest (ROI). The single peak 1Ddistributions obtained from separately analyzing these ROIs using datafrom 1D experiments were then averaged according to their relative spindensities and taken as the 1D ground truth (shown in FIGS. 7A-7B). Themarginal T₁ and D distributions from a ROI that included all three (T₁,D) samples using 1D experiments are shown in FIGS. 7A-7B, respectively.These 1D estimations were used to constrain the 2D D-T₁ distributionreconstruction, resulting in a stabilization and acceleration.

Accuracy of the 2D Distribution Derived Parameters

The accuracy of the estimated parameters, D-T₁, and the height of eachpeak, F, in the obtained 2D distributions, was determined by computingtheir normalized root mean square error (nrmse), E, relative to theground truth values (shown in Table 1 below), wherein

${\epsilon = {\frac{\sqrt{\langle ( {{{Estimated}{value}} - {{True}{value}}} )^{2} \rangle}}{{True}{value}} \times 100}},$where

represents the geometrical mean (gm). The MADCO approach outperformedconventional methods in almost every instance, while consistentlykeeping E well-below 10%.

The nrmse values of single parameters from the distribution may bemisleading. For example, the nrmse associated with the first two peaksin the spectrum obtained by using the conventional method with 64acquisitions points to reasonable accuracy (see Table 1 below)—whileexamination of the actual 2D spectrum (FIG. 3A) clearly shows that thesepeaks are not even resolved.

The actual estimated parameters, gmD, gmT₁, and the height of each peakF were determined by averaging over sections in the 2D distributionsthat correspond to the three distinct peaks. The partition between thepeaks was determined according to the ground truth distribution (FIG. 1): (1) 100≤T₁≤494 ms; (2) 543≤T₁≤954 ms; (3) 1048≤T₁≤10000 ms. Note thatthe entire range of diffusivities was included in each peak. Theresulting parameters are detailed in Table 2 below.

TABLE 1 Accuracy of the estimated 2D distribution derived parameters forthe MADCO and convention methods at different number of acquisitions.The nrmse [%] pf the gmT₁, gmD, and F estimates of each peak relative tothe ground truth values are expressed as E_(T1), E_(D), and E_(F),respectively. G4 Acq. 157 Acq. 1480 Acq. Peak MADCO/Conv. MADCO/Conv.MADCO/Conv. 1 ϵ_(T) ₁ 3.7/7.6 0.4/4.3 0.7/5.9 ϵ_(D) 8.8/1.5 0.8/2.02.9/4.3 ϵ_(F) 1.8/1.1 8.4/6.1 8.6/1.7 2 ϵ_(D) 9.1/6.8 6.2/2.7 3.6/14ϵ_(F) 0.1/25 8.9/31 6.1/63 3 ϵ_(T) ₁ 12/9.2 2.9/12 1.1/18 ϵ_(D) 13/292.3/11 2.3/14 ϵ_(F) 5.4/30 5.4/43 2.9/75

TABLE 2 Accuracy of the estimated 2D distribution derived parameters forthe MADCO and conventional methods at different number of acquisitions.64 Acq. 157 Acq. 1480 Acq. Truth MADCO Conv. MADCO Conv. MADCO Conv.Peak 1 gmT₁ [ms] 293 304 316 292 306 291 311 gmD [μm²/ms] 2.26 2.06 2.232.25 2.22 2.20 2.17 F 0.35 0.33 0.34 0.32 0.33 0.32 0.34 Peak 2 gmD[μm²/ms] 1.99 1.81 1.86 1.87 1.94 1.92 2.27 F 0.35 0.35 0.26 0.38 0.240.37 0.13 Peak 3 gmT₁ [ms] 1596 1785 1449 1643 1406 1613 1307 gmD[μm²/ms] 1.24 1.39 1.36 1.26 1.37 1.26 1.41 F 0.30 0.32 0.40 0.30 0.430.31 0.53

Measuring Molecular Exchange

The disclosed technology can also be used to probe dynamic migration ofwater from one domain to another, referred to as molecular exchange.Rather than impose unsupported assumptions about the number ofexchanging compartments, the disclosed technology provides a model-freeapproach to measure exchange, allowing for any number of exchangeprocesses between any number of compartments. This fast diffusionexchange spectroscopy (fDEXSY) MRI method provides a framework to obtainsuch information, which can be translated into cell membranepermeability, in a clinically feasible time frame.

Other Exemplary Applications of Technology

The disclosed technology can be in clinical MRI scanners, and can beused to perform in vivo microdynamic MRI scans. The differentmicrodynamic biomarkers can alter significantly with brain tissueviability. Particularly useful applications of microdynamic MRI includeexamining changes in inflammation, cancer, and stroke. Otherapplications include investigating neuroplasticity in normal developmentand learning. Because the main processes during neuroplasticity includeinduction of long-term potentiation, neurogenesis, and structuralremodeling of various cellular and subcellular components, thespecificity and sensitivity of microdynamic MRI to nervous tissuecomponent density and exchange make it an excellent exploratory tool.MRI tools based on the disclosed technology can also be applied tonormal and injured ex vivo tissue samples to measure the exchange withacquired high resolution and high quality MRI data. Quantitativehistology and immunohistochemistry can then be performed on the samespinal cord and brain tissue specimens for direct comparison of thedistribution of various tissue stains with MRI-based permeability maps.

The disclosed methods and apparatus can be applied in whole-body imageapplications to scan organs including but not limited to the heart,placenta, liver, kidneys, spleen, colon, prostate, as well as skeletaland other muscles and peripheral nerves. The disclosed approaches canalso be used in genotype/phenotype and other studies using vertebratesand other animal models as well as with non-biological materials,including foods, organic and synthetic polymers and gels, separationsystems used in chemical engineering applications, soil and othersamples, clay and rock, and other porous and non-porous media. Thedisclosed methods and apparatus can be applied to ex vivo and in vitroapplications to evaluation of specimens such as animals, plants,micro-organisms, or other organisms, or selected organs or otherportions of such organisms.

In other applications, the disclosed methods and apparatus can be usedin studying abnormal and normal developmental trajectories as well as avariety of disorders, diseases and sequelae of trauma, including mildtraumatic brain injury, to follow and assess inflammatory responses insoft tissues, including the brain, in which immune and other cells mayinfiltrate into the extracellular matrix (ECM), and in evaluating andtracking wound healing and other time dependent cellular and tissueprocesses. As noted above, the disclosed methods and apparatus can alsobe used to study development of other tissues and organs in humans oranimals or other living organisms either ex vivo or in vivo as well asnon-biological specimens.

While examples of the disclosed technology are generally discussed withreference to magnetic resonance imaging (MRI), the disclosed approachescan also be used in nuclear magnetic resonance (NMR) generally, or anymagnetic resonance based specimen evaluation, with or without imageacquisition.

Additional information regarding the disclosed technology and itspossible application can be found in Bai R, Benjamini D, Cheng J, andBasser P J: “Fast, accurate 2D-MR relaxation exchange spectroscopy(REXSY): beyond compressed sensing,” published in The Journal ofChemical Physics, 145:154202, 2016; and in Benjamini D and Basser P J:“Towards clinically feasible relaxation-diffusion correlation MRI usingMADCO,” published in Microporous and Mesoporous Materials, 2017,10.1016/j.micromeso.2017.02.001; both of which are incorporated hereinby reference in their entireties.

Signal Acquisition

NMR signals as described herein can be obtained using an exemplaryNMR/MRI apparatus 300 as illustrated in FIG. 8 . The apparatus 300includes a controller/interface 302 that can be configured to applyselected magnetic fields such as constant or pulsed fields to a target(e.g., a patient or specimen). An axial magnet controller 304 is incommunication with an axial magnet 306 that is generally configured toproduce a substantially constant magnetic field Bo. A gradientcontroller 308 is configured to apply a constant or time-varyinggradient magnetic field in a selected direction or in a set ofdirections using magnet coils 310-312 to produce respective magneticfield gradients G_(x), G_(y), G_(z) or combinations thereof. An RFgenerator 314 is configured to deliver one or more RF pulses to a targetusing a transmitter coil 315. An RF receiver 316 is in communicationwith a receiver coil 318 and is configured to detect or measure netmagnetization of spins. Slice selection gradients can be applied withthe same hardware used to apply the diffusion gradients.

The gradient controller 308 can be configured to produce pulses or othergradient fields along one or more axes. By selection of such gradientsas described in, for example, U.S. Pat. No. 5,539,310, an effectivediffusion tensor can be found. In addition, the gradient controller 308can be configured to apply gradient pulses or other gradient magneticfields of different magnitudes, and associated MR signals can bedetected by the RF receiver 316. A computer 324 or other processingsystem such as a personal computer, a workstation, a mobile computingdevice, or a networked computer can be provided for data acquisition,control and/or analysis. The computer 324 may include a hard disk, aremovable storage medium such as a floppy disk or CD-ROM, and/or othermemory such as random access memory (RAM). Computer-executableinstructions for data acquisition or control can be provided on any formof tangible data storage media, and/or delivered to the computer 324 viaa local area network, the Internet, or other network. Signalacquisition, instrument control, and signal analysis can be performedwith distributed processing. For example, signal acquisition and signalanalysis or processing can be performed at different locations.

MR signals can be obtained for a variety of gradient magnitudes anddirections. Signals can be obtained by fixing a magnitude and durationof an applied pulsed-gradient magnetic field or effective magnitude ofother spin-encoding magnetic field, and varying the direction in whichthe encoding field is applied. After signals associated with the variousdirections are obtained, spin encoding field magnitude is changed andanother series of signals at the various directions is obtained.Alternatively, signals can be obtained by fixing the direction of theapplied encoding field and varying encoding field magnitudes.

Other signal acquisition sequences can also be used. For example, whiledouble PFG NMR and MRI acquisition are described in this application, inparticular, other forms of multiple PFG NMR and MRI experiments can beused. In addition, diffusion encoding can occur prior to the MRIacquisition as a “filter” or it can be embedded or incorporated withinthe imaging sequence itself.

A representative method 400 is illustrated in FIG. 9 for reconstructinga two-dimensional spectrum from a reduced amount of acquired NMRspecimen data using one-dimensional marginal distributions of the dataset as constraints. At 402, the exemplary method 400 can compriseacquiring a selected set of NMR specimen data by varying at least afirst NMR acquisition parameter and a second NMR acquisition parameterover first and second ranges, respectively, so that the acquired NMRspecimen data is associated with a first specimen characteristic and asecond specimen characteristic. At 404, the method 400 can comprisedetermining a first marginal distribution and a second marginaldistribution associated with the first and second specimencharacteristics based on the acquired NMR specimen data, wherein thefirst and second marginal distributions depend on a selected combinationof the first and second NMR acquisition parameters. Then, at 406, themethod 400 can comprise reconstructing a two-dimensional spectrumassociated with the specimen using the first and second marginaldistributions as constraints. The first and second marginaldistributions can comprise one-dimensional marginal distributionsassociated with the first NMR acquisition parameter and the second NMRacquisition parameter, respectively. In some embodiments of the method400, the first specimen characteristic is diffusivity (D) and the secondspecimen characteristic is spin-lattice relaxation time (T1), forexample. In some embodiments of the method 400, the first NMRacquisition parameter can be b-value and the second NMR acquisitionparameter can be inversion time τ, for example. In some embodiments ofthe method 400, the first and second specimen characteristics cancomprise diffusivity (D), spin-lattice relaxation time (T1), spin-spinrelaxation time (T2), and/or combinations thereof. The one-dimensionalmarginal distributions can be used as either soft constraints or hardconstraints. In some embodiments of the method 400, the first and secondmarginal distributions can be determined using l1 regularization. At408, the method 400 can comprise optionally repeating the process of402, 404, 406 with additional acquired NMR specimen data to improveaccuracy of reconstructed 2D spectrum in 406. The method 400 describessteps performed in an exemplary order, but these steps can be performedin different orders, and the acquisitions of NMR specimen data and/orthe determinations of marginal distributions that are described as beingperformed in a common step can alternatively be performed in two or moredifferent steps.

More information regarding NMR data acquisition, determination ofmarginal distributions, and reconstruction of multi-dimensional spectracan be found in U.S. Pat. Nos. 7,643,863 and 8,380,280, which areincorporated by reference herein in their entirety. More informationrelated to the determination of joint higher dimensional distributionsbased on lower dimensional distributions in the NMR/MRI field can befound in WO 2016/086025, published Jun. 2, 2016, which is incorporatedherein by reference in its entirety.

Computational Environment

FIG. 10 and the following discussion are intended to provide a brief,general description of an exemplary computing environment in which thedisclosed technology may be implemented. Although not required, thedisclosed technology is described in the general context of computerexecutable instructions, such as program modules, being executed by apersonal computer (PC). Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types. Moreover,the disclosed technology may be implemented with other computer systemconfigurations, including hand held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, and the like. The disclosedtechnology may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 10 , an exemplary system for implementing thedisclosed technology includes a general purpose computing device in theform of an exemplary conventional PC 500, including one or moreprocessing units 502, a system memory 504, and a system bus 506 thatcouples various system components including the system memory 504 to theone or more processing units 502. The system bus 506 may be any ofseveral types of bus structures including a memory bus or memorycontroller, a peripheral bus, and a local bus using any of a variety ofbus architectures. The exemplary system memory 504 includes read onlymemory (ROM) 508 and random access memory (RAM) 510. A basicinput/output system (BIOS) 512, containing the basic routines that helpwith the transfer of information between elements within the PC 500, isstored in ROM 508.

The exemplary PC 500 further includes one or more storage devices 530such as a hard disk drive for reading from and writing to a hard disk, amagnetic disk drive for reading from or writing to a removable magneticdisk, and an optical disk drive for reading from or writing to aremovable optical disk (such as a CD-ROM or other optical media). Suchstorage devices can be connected to the system bus 506 by a hard diskdrive interface, a magnetic disk drive interface, and an optical driveinterface, respectively. The drives and their associated computerreadable media provide nonvolatile storage of computer-readableinstructions, data structures, program modules, and other data for thePC 500. Other types of computer-readable media which can store data thatis accessible by a PC, such as magnetic cassettes, flash memory cards,digital video disks, CDs, DVDs, RAMs, ROMs, and the like, may also beused in the exemplary operating environment. As shown in FIG. 10 ,instructions for NMR data acquisition, acquired data, computeddistributions, and processor-executable instructions for determiningmulti-dimensional spectra using lower dimensional spectra as constraintsare stored in a memory portion 590 but storage and processing can beprovided at other locations, such as locally at an NMR machine or at aremote location accessible via a wide area network.

A number of program modules may be stored in the storage devices 530including an operating system, one or more application programs, otherprogram modules, and program data. A user may enter commands andinformation into the PC 500 through one or more input devices 540 suchas a keyboard and a pointing device such as a mouse. Other input devicesmay include a digital camera, microphone, joystick, game pad, satellitedish, scanner, or the like. These and other input devices are oftenconnected to the one or more processing units 502 through a serial portinterface that is coupled to the system bus 506, but may be connected byother interfaces such as a parallel port, game port, or universal serialbus (USB). A monitor 546 or other type of display device is alsoconnected to the system bus 506 via an interface, such as a videoadapter. Other peripheral output devices, such as speakers and printers(not shown), may be included.

The PC 500 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer560. In some examples, one or more network or communication connections550 are included. The remote computer 560 may be another PC, a server, arouter, a network PC, or a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the PC 500, although only a memory storage device 562 has beenillustrated in FIG. 10 . The personal computer 500 and/or the remotecomputer 560 can be connected to a logical a local area network (LAN)and a wide area network (WAN). Such networking environments arecommonplace in offices, enterprise wide computer networks, intranets,and the Internet.

When used in a LAN networking environment, the PC 500 is connected tothe LAN through a network interface. When used in a WAN networkingenvironment, the PC 500 typically includes a modem or other means forestablishing communications over the WAN, such as the Internet. In anetworked environment, program modules depicted relative to the personalcomputer 500, or portions thereof, may be stored in the remote memorystorage device or other locations on the LAN or WAN. The networkconnections shown are exemplary, and other means of establishing acommunications link between the computers may be used.

For purposes of this description, certain aspects, advantages, and novelfeatures of the embodiments of this disclosure are described herein. Thedisclosed methods and systems should not be construed as limiting in anyway. Instead, the present disclosure is directed toward all novel andnonobvious features and aspects of the various disclosed embodiments,alone and in various combinations and sub-combinations with one another.The methods, apparatuses, and systems are not limited to any specificaspect or feature or combination thereof, nor do the disclosedembodiments require that any one or more specific advantages be presentor problems be solved.

Although the operations of some of the disclosed methods are describedin a particular, sequential order for convenient presentation, it shouldbe understood that this manner of description encompasses rearrangement,unless a particular ordering is required by specific language. Forexample, operations described sequentially may in some cases berearranged or performed concurrently. Moreover, for the sake ofsimplicity, the specification and attached figures may not show all thevarious ways in which the disclosed methods can be used in conjunctionwith other methods.

The singular terms “a”, “an”, and “the” include plural referents unlesscontext clearly indicates otherwise. The term “comprises” means“includes without limitation.” The term “coupled” means physicallylinked and does not exclude intermediate elements between the coupledelements. The term “and/or” means any one or more of the elementslisted. Thus, the term “A and/or B” means “A”, “B” or “A and B.”

In view of the many possible embodiments to which the principles of thedisclosed technology may be applied, it should be recognized that theillustrated embodiments are only preferred examples and should not betaken as limiting the scope of the disclosure. Rather, the scope of thedisclosure is at least as broad as the following claims. We thereforeclaim all that comes within the scope of the following claims.

The invention claimed is:
 1. A method, comprising: acquiring a selectedset of magnetic resonance (MR) specimen data by varying at least a firstMR acquisition parameter and a second MR acquisition parameter overfirst and second ranges, respectively, so that the acquired MR specimendata is associated with a first specimen characteristic and a secondspecimen characteristic; based on the acquired MR specimen data,determining a first marginal distribution and a second marginaldistribution associated with the first and second specimencharacteristics, the first and second marginal distributions dependenton a selected combination of the first and second MR acquisitionparameters, and wherein the first marginal distribution is ann-dimensional marginal distribution and the second marginal distributionis an m-dimensional marginal distribution; and reconstructing a an(n+m)-dimensional spectrum associated with the specimen using the firstand second marginal distributions as constraints.
 2. The method of claim1, wherein the first marginal distribution is a one-dimensional marginaldistribution associated with the first MR acquisition parameter and thesecond marginal distribution is a two-dimensional marginal distributionassociated with the second MR acquisition parameter, and wherein thereconstructed (n+m)-dimensional spectrum is a three-dimensionalspectrum.
 3. The method of claim 1, further comprising: acquiring atwo-dimensional specimen data set associated with both the first MRacquisition parameter and the second MR acquisition parameter, whereinthe two- dimensional spectrum is reconstructed using the two-dimensionaldata set.
 4. The method of claim 1, wherein the first specimencharacteristic is diffusivity (D) and the second specimen characteristicis spin-lattice relaxation time (T1).
 5. The method of claim 1, whereinthe first MR acquisition parameter is b-value and the second MRacquisition parameter is inversion time τ.
 6. The method of claim 1,wherein the first and second specimen characteristics comprisediffusivity (D), spin-lattice relaxation time (T1), spin-spin relaxationtime (T2), or combinations thereof.
 7. A magnetic resonance (MR) systemcomprising: an MR data acquisition system; and an MR data processorcoupled to the MR data acquisition system and operable to reconstruct(n+m)-dimensional MR spectra associated with specimens using first andsecond marginal distributions as constraints, wherein the first marginaldistribution is an n-dimensional marginal distribution and the secondmarginal distribution is an m-dimensional marginal distribution.
 8. TheMR system of claim 7, wherein the MR data acquisition system is operableto acquire MR data based on applied MR signals associated with first andsecond MR acquisition parameters.
 9. The MR system of claim 7, whereinthe first and second MR acquisition parameters are operable to produceMR signal variations associated with a first specimen characteristic anda second specimen characteristic.
 10. The MR system of claim 7, whereinthe MR data acquisition system includes at least a magnet situated toproduce a static magnetic field and a radio-frequency pulse generatoroperable to induce specimen spin rotations.
 11. The MR system of claim7, wherein the first specimen characteristic is diffusivity (D) and thesecond specimen characteristic is spin-lattice relaxation time (T1). 12.The MR system of claim 7, wherein the first MR acquisition parameter isb-value and the second MR acquisition parameter is inversion time (τ).13. The MR system of claim 7, wherein the first and second specimencharacteristics comprise diffusivity (D), spin-lattice relaxation time(T1), spin-spin relaxation time (T2), or combinations thereof.